ELICIT creates free-viewpoint motion videos from a single image by constructing an animatable NeRF representation in one-shot learning.

Official repository of "One-shot Implicit Animatable Avatars with Model-based Priors".

[Arxiv] [Website]

1. The data-efficient pipeline of creating a 3D animatable avatar from a single image.
2. Use CLIP-based semantic loss to infer the entire 3D appearance of the human body with the help of a rough SMPL shape.
3. A segmentation-based sampling strategy to create more realistic visual details and geometries for 3D avatars.

Please follow the Installation Instruction to setup all the required packages.

We provide result videos in our webpage for the qualitative and quantitative evaluations in our paper. We also provided checkpoints for those experiments in Google Drive.

For the datasets we use for quantitative evaluations (ZJU-MoCAP, Human 3.6M), please prepare the original datasets into the same format as ZJU-MoCAP. Then use our scripts in tools to preprocess the dataset and render SMPL meshes for training.

For customized single-image data, we provides examples from DeepFashion datasets in dataset/fashion.

See more details in Data Instruction.

python train.py --cfg configs/elicit/zju_mocap/377/smpl_init_texture.yaml 
python train.py --cfg configs/elicit/zju_mocap/377/finetune.yaml

We also provide checkpoints for all the subjects in Google Drive, please unzip the file in the following structure:

${ELICIT_ROOT}
    └── experiments
        └── elicit
            ├── zju_mocap
            ├── h36m
            └── fashion

Evaluate novel pose synthesis.

python run.py --type movement --cfg configs/elicit/zju_mocap/377/finetune.yaml 

Evaluate novel view synthesis.

python run.py --type freeview --cfg configs/elicit/zju_mocap/377/finetune.yaml freeview.use_gt_camera True

Freeview rendering on arbitrary frames.

python run.py --type freeview  --cfg configs/elicit/zju_mocap/377/finetune.yaml freeview.frame_idx $FRAME_INDEX_TO_RENDER

The rendered frames and video will be saved at experiments/zju_mocap/377/latest.

@article{huang2022one,
  title={One-shot Implicit Animatable Avatars with Model-based Priors},
  author={Huang, Yangyi and Yi, Hongwei and Liu, Weiyang and Wang, Haofan and Wu, Boxi and Wang, Wenxiao and Lin, Binbin and Zhang, Debing and Cai, Deng},
  journal={arXiv preprint arXiv:2212.02469},
  year={2022}
}

Our implementation is mainly based on HumanNeRF, and took reference from Animatable NeRF and AvatarCLIP. We thanks the authors for their open source contributions. In addition, we thank the authors of Animatble NeRF for their help in the data preprocessing of Human 3.6M.